Machine Learning
159 learners
Navigating Data Simplification with PCA
Grasp the essentials of dimensionality reduction and lay the groundwork for your journey by understanding and implementing Principal Component Analysis (PCA) using Python's Scikit-learn. This launchpad course provides a comprehensive introduction into why, how and when to use PCA for feature extraction and enhancing computational efficiency in high-dimensional data sets.
MatPlotLib
Numpy
Python
Scikit-learn
4 lessons
14 practices
3 hours
Feature Engineering
Lessons and practices
Visualizing Dimensionality Reduction with PCA
Navigating Dimensionality: Simplifying to One Principal Component
Crafting the PCA Function
Visualizing Eigenvectors and Dataset Variance
Charting the Stars: Eigenvector Visualization
Unveiling the Directions of Variance with Eigendecomposition
Charting the Celestial Heights: Scatter Plot Practice
Visualizing Dimensional Reduction with PCA
Adjusting PCA to One Principal Component
Navigating the PCA Space: Dimensionality Reduction and Visualization
Crafting PCA from Ground Up
Visualizing Data with PCA and Logistic Regression
Reducing Dimensions with PCA
Integrate PCA in Logistic Regression Model
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